Wednesday, August 17, 2011

Beyond IQ series: The "big picture" model of educational productivity context for the Model of Academic Competence and Motivation (MACM)

Background comment regarding this series

Interest in social-emotional learning and resiliency training (click here and here for just two examples) in education has shown a recent uptick on activity. Given this activity, IQs Corner is starting a series to explain the previously articulated Model of Academic Competence and Motivation (MACM), which was a model ahead of it's time (IMHO). The imporance of non-cognitive (conative) characteristics in learning have been articulated since the days of Spearman, the father of the construct of general intelligence. Richard Snow's work on the concept of "aptitude," which integrates cognitive and conative individual difference variables, is the foundation of the Beyond IQ MACM. Non-cognitive (cognitive) characteristics of learners are important for learning and are more manipulable (more likely to be modified via intervention) than intelligence. Thus, the MACM components make sense as potential levers for improving school learning and pursuing more well rounded life-long learners. This material comes a larger set of materials on the web (click here).

Current MACM Series Installment

This second installment in the Beyond IQ series provides the the over-arching empirical and theoretical backdrop that led to the development of the MACM framework. [All installments in this series (and other related posts and research) can be found by clicking here]. Research on models of school learning, and the seminal work represented by Walberg's theory of educational productivity, provided the "big picture" framework for the development of one component of this larger model of educational productivity--the MACM framework.

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Walberg's (1981) theory of educational productivity, which is one of the few empirically tested theories of school learning based on an extensive review and integration of over 3,000 studies (DiPerna, Volpe & Stephen, 2002). “Wang, Haertel, and Walberg (1997) analyzed the content of 179 handbook chapters and reviews and 91 research syntheses and surveyed educational researchers in an effort to achieve some consensus regarding the most significant influences on learning" (Greenberg et al., 2003, p. 470). Using a variety of methods, Wang, et al. (1977) identified 28 categories of learning influence. Of the 11 most influential domains of variables, 8 involved social-emotional influences: classroom management, parental support, student- teacher interactions, social- behavioral attributes, motivational- effective attributes, the peer group, school culture, and classroom climate (Greenberg et al., 2003). Distant background influences (e.g., state, district, or school policies, organizational characteristics, curriculum, and instruction) were less influential. Wang et al. (1997) concluded that "the direct intervention in the psychological determinants of learning promise the most effective avenues for reform" (p. 210). Wang et al.’s research review targeted student learning characteristics (i.e., social, behavioral, motivational, affective, cognitive, and metacognitive) as the set of variables with the most potential for modification that could, in turn, significantly and positively effect student outcomes (DiPerna et al., 2002).

More recently, Zins, Weissberg, Wang and Walberg, (2004) demonstrated the importance of the domains of motivational orientations, self-regulated learning strategies, and social/interpersonal abilities in facilitating academic performance. Zins et al. reported, based on the large-scale implementation of a Social-Emotional Learning (SEL) program, that student’s who became more self-aware and confident regarding their learning abilities, who were more motivated, who set learning goals, and who were organized in their approach to work (self- regulated learning) performed better in school. According to Greenberg, Weissberg, O'Brien, Zins, Fredericks, Resnick, & Elias, (2003), Zins et al. (2004) assert that “research linking social, emotional, and academic factors are sufficiently strong to advance the new term social, emotional, and academic learning (SEAL). A central challenge for researchers, educators, and policymakers is to strengthen this connection through coordinated multiyear programming"(p. 470).

Walberg and associates’ conclusions resonate with findings from other fields. For example, the "resilience" literature (Garmezy, 1993) grew from the observation that despite living in disadvantaged and risky environments, certain children overcame and attained high levels of achievement, motivation, and performance (Gutman, Sameroff & Eccles, 2002). Wach’s (2000) review of biological, social, and psychological factors suggested that no single factor could explain “how” and “why” these resilient children had been inoculated from the deleterious effects of their day- to-day environments. A variety of promotive (direct) and protective (interactive) variables were suggested, which included, aside from cognitive abilities, such conative characteristics as study habits, social abilities, and the absence of behavior problems (Guttman et al., 2003).

Classroom learning is a multiplicative, diminishing-returns function of four essential factors—student ability and motivation, and quality and quantity of instruction—and possibly four supplementary or supportive factors—the social psychological environment of the classroom, education-stimulating conditions in the home and peer group, and exposure to mass media. Each of the essential factors appears to be necessary but insufficient by itself for classroom learning; that is, all four of these factors appear required at least at minimum level. It also appears that the essential factors may substitute, compensate, or trade off for one another in diminishing rates of return: for example, immense quantities of time may be required for a moderate amount of learning to occur if motivation, ability, or quality of instruction is minimal (Haertel et al., 1983, p. 76)

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An important finding of the Walberg et al. large scale causal modeling research was that nine different educational productivity factors were hypothesized to operate vis- à-vis a complex set of interactions to account for school learning. Additionally, some student characteristic variables (motivation, prior achievement, attitudes) had indirect effects (e.g., the influence of the variable “went through” or was mediated via another variable).

The importance of the Walberg et al. group’s findings cannot be overstated. Walberg’s (1981) theory of educational productivity is one of the few empirically tested theories of school learning and is based on the review and integration of over 3,000 studies (DiPerna et al., 2002). Walberg et al. have identified key variables that effect student outcomes: student ability/prior achievement, motivation, age/developmental level, quantity of instruction, quality of instruction, classroom climate, home environment, peer group, and exposure to mass media outside of school (Walberg, Fraser & Welch, 1986). In the current context, the first three variables (ability, motivation, and age) reflect characteristics of the student. The fourth and fifth variables reflect instruction (quantity and quality), and the final four variables (classroom climate, home environment, peer group, and exposure to media) represent aspects of the psychological environment (DiPerna et al., 2002). Clearly student characteristics are important for school learning, but they only comprise a portion of the learning equation.

More recently, Wang, Haertel, and Walberg (1993) organized the relevant school learning knowledge base into major construct domains (State & District Governance & Organization, Home & Community Contexts, School Demographics, Culture, Climate, Policies & Practices, Design & Delivery of Curriculum & Instruction, Classroom Practices, Learner Characteristics) and attempted to establish the relative importance of 228 variables in predicting academic domains. Using a variety of methods, the authors concluded that psychological, instructional, and home environment characteristics (“proximal” variables) have a more significant impact on achievement than variables such as state-, district-, or school-level policy and demographics (“distal”variables). More importantly, in the context of the current document, student characteristics (i.e., social, behavioral, motivational, affective, cognitive, metacognitive) were the set of proximal variables with the most significant impact on learner outcomes (DiPerna et al., 2002).

A sampling of the major components of the school learning models summarized by Walberg and associates is presented in the figure below (double on figure to enlarge or click here for another on-line version). The student characteristic domain in the figure is the primary focus of this series and the MACM framework.

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About Me

Dr. Kevin McGrew is Director of the Institute for Applied Psychometrics (llc). Additional information, including potential conflicts of interest resulting from commercial test development or other consultation, can be found at The MindHub(TM; http://www.themindhub.com ). General email contact is iap@earthlink.net.